This paper considers the use of smooth transition autoregressive models for forecasting. First, the modelling of time series with these nonlinear models is discussed. Techniques for obtaining multiperiod forecasts are presented. The usefulness of forecast densities in the case of nonlinear...

This note develops general model-free adjustment procedures for the calculation of unbiased volatility loss functions based on practically feasible realized volatility benchmarks. The procedures, which exploit the recent asymptotic distributional results in Barndorff-Nielsen and Shephard...

The behavior of agricultural commodity markets can arguably result in markedly asymmetricprice cycles, that is, downward cycles of substantially different length and breadth thanupward cycles. This study assesses whether asymmetric-cycle models can enhance the understandingof the dynamics and...

In this paper, a time series forecasting approach by integrating particle swarm optimization (PSO) and support vector regression (SVR) is proposed. SVR has been widely applied in time series predictions. However, no general guidelines are available to choose the free parameters of an SVR model....

This study develops a framework to forecast India's gross domestic product growth on a quarterly frequency from 2004 to 2018. The models, which are based on real and monetary sector descriptions of the Indian economy, are estimated using Bayesian vector autoregression (BVAR) techniques. The real...